Technologies for coordinating disaggregated accelerator device resources

ABSTRACT

A compute device to manage workflow to disaggregated computing resources is provided. The compute device comprises a compute engine receive a workload processing request, the workload processing request defined by at least one request parameter, determine at least one accelerator device capable of processing a workload in accordance with the at least one request parameter, transmit a workload to the at least one accelerator device, receive a work product produced by the at least one accelerator device from the workload, and provide the work product to an application.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of U.S. patent applicationSer. No. 15/721,833, filed Sep. 30, 2017, and claims the benefit of U.S.Provisional Patent Application No. 62/427,268, filed Nov. 29, 2016, andIndian Provisional Patent Application No. 201741030632, filed Aug. 30,2017.

BACKGROUND

In modern cloud environments, compute devices (sometimes referred to ascompute sleds) host many computer applications (e.g., workloads) thateach perform specific functions. Each application requires processingpower to complete various application tasks (e.g., functions, processes,operations within a workload), such as data processing but also inputand output tasks such as displaying data, receiving data, or the like.Many of the abovementioned tasks are processed using computer programsthat embody complex logic sequences. Such logic sequences often executefaster when carried out by a specialized component, such as a fieldprogrammable gate array, an application specific integrated circuit(ASIC), or other device specifically configured for performing suchcomputation (e.g., an accelerator device). An accelerator device may beconfigured using, for example, a hardware definition language, toperform tasks assigned by a computer application.

Given that accelerator devices typically specialize in executing aparticular type of task (e.g., encryption, compression, etc.), anoperating system of the compute device, or the application executing onthe compute device, typically must identify the available features ofthe accelerator device(s) and manage communications with the acceleratordevice(s), taking away from compute resources (e.g., processor cycles)that could otherwise be spent on executing the application.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a diagram of a conceptual overview of a data center in whichone or more techniques described herein may be implemented according tovarious embodiments;

FIG. 2 is a diagram of an example embodiment of a logical configurationof a rack of the data center of FIG. 1;

FIG. 3 is a diagram of an example embodiment of another data center inwhich one or more techniques described herein may be implementedaccording to various embodiments;

FIG. 4 is a diagram of another example embodiment of a data center inwhich one or more techniques described herein may be implementedaccording to various embodiments;

FIG. 5 is a diagram of a connectivity scheme representative oflink-layer connectivity that may be established among various sleds ofthe data centers of FIGS. 1, 3, and 4;

FIG. 6 is a diagram of a rack architecture that may be representative ofan architecture of any particular one of the racks depicted in FIGS. 1-4according to some embodiments;

FIG. 7 is a diagram of an example embodiment of a sled that may be usedwith the rack architecture of FIG. 6;

FIG. 8 is a diagram of an example embodiment of a rack architecture toprovide support for sleds featuring expansion capabilities;

FIG. 9 is a diagram of an example embodiment of a rack implementedaccording to the rack architecture of FIG. 8;

FIG. 10 is a diagram of an example embodiment of a sled designed for usein conjunction with the rack of FIG. 9;

FIG. 11 is a diagram of an example embodiment of a data center in whichone or more techniques described herein may be implemented according tovarious embodiments;

FIG. 12 is a simplified block diagram of at least one embodiment of asystem for managing disaggregated accelerator resources using a pooledsystem management engine (PSME) device;

FIG. 13 is a simplified block diagram of at least one embodiment of acompute sled of the system of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of anenvironment that may be established by the compute sled of FIGS. 12 and13; and

FIGS. 15-18 are a simplified flow diagram of at least one embodiment ofa method for managing disaggregated accelerator resources that may beperformed by the compute sled of FIGS. 12 and 13.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

FIG. 1 illustrates a conceptual overview of a data center 100 that maygenerally be representative of a data center or other type of computingnetwork in/for which one or more techniques described herein may beimplemented according to various embodiments. As shown in FIG. 1, datacenter 100 may generally contain a plurality of racks, each of which mayhouse computing equipment comprising a respective set of physicalresources. In the particular non-limiting example depicted in FIG. 1,data center 100 contains four racks 102A to 102D, which house computingequipment comprising respective sets of physical resources (PCRs) 105Ato 105D. According to this example, a collective set of physicalresources 106 of data center 100 includes the various sets of physicalresources 105A to 105D that are distributed among racks 102A to 102D.Physical resources 106 may include resources of multiple types, suchas—for example—processors, co-processors, accelerators, fieldprogrammable gate arrays (FPGAs), memory, and storage. The embodimentsare not limited to these examples.

The illustrative data center 100 differs from typical data centers inmany ways. For example, in the illustrative embodiment, the circuitboards (“sleds”) on which components such as CPUs, memory, and othercomponents are placed are designed for increased thermal performance. Inparticular, in the illustrative embodiment, the sleds are shallower thantypical boards. In other words, the sleds are shorter from the front tothe back, where cooling fans are located. This decreases the length ofthe path that air must to travel across the components on the board.Further, the components on the sled are spaced further apart than intypical circuit boards, and the components are arranged to reduce oreliminate shadowing (i.e., one component in the air flow path of anothercomponent). In the illustrative embodiment, processing components suchas the processors are located on a top side of a sled while near memory,such as DIMMs, are located on a bottom side of the sled. As a result ofthe enhanced airflow provided by this design, the components may operateat higher frequencies and power levels than in typical systems, therebyincreasing performance. Furthermore, the sleds are configured to blindlymate with power and data communication cables in each rack 102A, 102B,102C, 102D, enhancing their ability to be quickly removed, upgraded,reinstalled, and/or replaced. Similarly, individual components locatedon the sleds, such as processors, accelerators, memory, and data storagedrives, are configured to be easily upgraded due to their increasedspacing from each other. In the illustrative embodiment, the componentsadditionally include hardware attestation features to prove theirauthenticity.

Furthermore, in the illustrative embodiment, the data center 100utilizes a single network architecture (“fabric”) that supports multipleother network architectures including Ethernet and Omni-Path. The sleds,in the illustrative embodiment, are coupled to switches via opticalfibers, which provide higher bandwidth and lower latency than typicaltwisted pair cabling (e.g., Category 5, Category 5e, Category 6, etc.).Due to the high bandwidth, low latency interconnections and networkarchitecture, the data center 100 may, in use, pool resources, such asmemory, accelerators (e.g., graphics accelerators, FPGAs, ASICs, etc.),and data storage drives that are physically disaggregated, and providethem to compute resources (e.g., processors) on an as needed basis,enabling the compute resources to access the pooled resources as if theywere local. The illustrative data center 100 additionally receivesutilization information for the various resources, predicts resourceutilization for different types of workloads based on past resourceutilization, and dynamically reallocates the resources based on thisinformation.

The racks 102A, 102B, 102C, 102D of the data center 100 may includephysical design features that facilitate the automation of a variety oftypes of maintenance tasks. For example, data center 100 may beimplemented using racks that are designed to be robotically-accessed,and to accept and house robotically-manipulatable resource sleds.Furthermore, in the illustrative embodiment, the racks 102A, 102B, 102C,102D include integrated power sources that receive a greater voltagethan is typical for power sources. The increased voltage enables thepower sources to provide additional power to the components on eachsled, enabling the components to operate at higher than typicalfrequencies.

FIG. 2 illustrates an exemplary logical configuration of a rack 202 ofthe data center 100. As shown in FIG. 2, rack 202 may generally house aplurality of sleds, each of which may comprise a respective set ofphysical resources. In the particular non-limiting example depicted inFIG. 2, rack 202 houses sleds 204-1 to 204-4 comprising respective setsof physical resources 205-1 to 205-4, each of which constitutes aportion of the collective set of physical resources 206 comprised inrack 202. With respect to FIG. 1, if rack 202 is representative of—forexample—rack 102A, then physical resources 206 may correspond to thephysical resources 105A comprised in rack 102A. In the context of thisexample, physical resources 105A may thus be made up of the respectivesets of physical resources, including physical storage resources 205-1,physical accelerator resources 205-2, physical memory resources 205-3,and physical compute resources 205-5 comprised in the sleds 204-1 to204-4 of rack 202. The embodiments are not limited to this example. Eachsled may contain a pool of each of the various types of physicalresources (e.g., compute, memory, accelerator, storage). By havingrobotically accessible and robotically manipulatable sleds comprisingdisaggregated resources, each type of resource can be upgradedindependently of each other and at their own optimized refresh rate.

FIG. 3 illustrates an example of a data center 300 that may generally berepresentative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. In theparticular non-limiting example depicted in FIG. 3, data center 300comprises racks 302-1 to 302-32. In various embodiments, the racks ofdata center 300 may be arranged in such fashion as to define and/oraccommodate various access pathways. For example, as shown in FIG. 3,the racks of data center 300 may be arranged in such fashion as todefine and/or accommodate access pathways 311A, 311B, 311C, and 311D. Insome embodiments, the presence of such access pathways may generallyenable automated maintenance equipment, such as robotic maintenanceequipment, to physically access the computing equipment housed in thevarious racks of data center 300 and perform automated maintenance tasks(e.g., replace a failed sled, upgrade a sled). In various embodiments,the dimensions of access pathways 311A, 311B, 311C, and 311D, thedimensions of racks 302-1 to 302-32, and/or one or more other aspects ofthe physical layout of data center 300 may be selected to facilitatesuch automated operations. The embodiments are not limited in thiscontext.

FIG. 4 illustrates an example of a data center 400 that may generally berepresentative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. As shown inFIG. 4, data center 400 may feature an optical fabric 412. Opticalfabric 412 may generally comprise a combination of optical signalingmedia (such as optical cabling) and optical switching infrastructure viawhich any particular sled in data center 400 can send signals to (andreceive signals from) each of the other sleds in data center 400. Thesignaling connectivity that optical fabric 412 provides to any givensled may include connectivity both to other sleds in a same rack andsleds in other racks. In the particular non-limiting example depicted inFIG. 4, data center 400 includes four racks 402A to 402D. Racks 402A to402D house respective pairs of sleds 404A-1 and 404A-2, 404B-1 and404B-2, 404C-1 and 404C-2, and 404D-1 and 404D-2. Thus, in this example,data center 400 comprises a total of eight sleds. Via optical fabric412, each such sled may possess signaling connectivity with each of theseven other sleds in data center 400. For example, via optical fabric412, sled 404A-1 in rack 402A may possess signaling connectivity withsled 404A-2 in rack 402A, as well as the six other sleds 404B-1, 404B-2,404C-1, 404C-2, 404D-1, and 404D-2 that are distributed among the otherracks 402B, 402C, and 402D of data center 400. The embodiments are notlimited to this example.

FIG. 5 illustrates an overview of a connectivity scheme 500 that maygenerally be representative of link-layer connectivity that may beestablished in some embodiments among the various sleds of a datacenter, such as any of example data centers 100, 300, and 400 of FIGS.1, 3, and 4. Connectivity scheme 500 may be implemented using an opticalfabric that features a dual-mode optical switching infrastructure 514.Dual-mode optical switching infrastructure 514 may generally comprise aswitching infrastructure that is capable of receiving communicationsaccording to multiple link-layer protocols via a same unified set ofoptical signaling media, and properly switching such communications. Invarious embodiments, dual-mode optical switching infrastructure 514 maybe implemented using one or more dual-mode optical switches 515. Invarious embodiments, dual-mode optical switches 515 may generallycomprise high-radix switches. In some embodiments, dual-mode opticalswitches 515 may comprise multi-ply switches, such as four-ply switches.In various embodiments, dual-mode optical switches 515 may featureintegrated silicon photonics that enable them to switch communicationswith significantly reduced latency in comparison to conventionalswitching devices. In some embodiments, dual-mode optical switches 515may constitute leaf switches 530 in a leaf-spine architectureadditionally including one or more dual-mode optical spine switches 520.

In various embodiments, dual-mode optical switches may be capable ofreceiving both Ethernet protocol communications carrying InternetProtocol (IP packets) and communications according to a second,high-performance computing (HPC) link-layer protocol (e.g., Intel'sOmni-Path Architecture's, Infiniband) via optical signaling media of anoptical fabric. As reflected in FIG. 5, with respect to any particularpair of sleds 504A and 504B possessing optical signaling connectivity tothe optical fabric, connectivity scheme 500 may thus provide support forlink-layer connectivity via both Ethernet links and HPC links. Thus,both Ethernet and HPC communications can be supported by a singlehigh-bandwidth, low-latency switch fabric. The embodiments are notlimited to this example.

FIG. 6 illustrates a general overview of a rack architecture 600 thatmay be representative of an architecture of any particular one of theracks depicted in FIGS. 1 to 4 according to some embodiments. Asreflected in FIG. 6, rack architecture 600 may generally feature aplurality of sled spaces into which sleds may be inserted, each of whichmay be robotically-accessible via a rack access region 601. In theparticular non-limiting example depicted in FIG. 6, rack architecture600 features five sled spaces 603-1 to 603-5. Sled spaces 603-1 to 603-5feature respective multi-purpose connector modules (MPCMs) 616-1 to616-5.

FIG. 7 illustrates an example of a sled 704 that may be representativeof a sled of such a type. As shown in FIG. 7, sled 704 may comprise aset of physical resources 705, as well as an MPCM 716 designed to couplewith a counterpart MPCM when sled 704 is inserted into a sled space suchas any of sled spaces 603-1 to 603-5 of FIG. 6. Sled 704 may alsofeature an expansion connector 717. Expansion connector 717 maygenerally comprise a socket, slot, or other type of connection elementthat is capable of accepting one or more types of expansion modules,such as an expansion sled 718. By coupling with a counterpart connectoron expansion sled 718, expansion connector 717 may provide physicalresources 705 with access to supplemental computing resources 705Bresiding on expansion sled 718. The embodiments are not limited in thiscontext.

FIG. 8 illustrates an example of a rack architecture 800 that may berepresentative of a rack architecture that may be implemented in orderto provide support for sleds featuring expansion capabilities, such assled 704 of FIG. 7. In the particular non-limiting example depicted inFIG. 8, rack architecture 800 includes seven sled spaces 803-1 to 803-7,which feature respective MPCMs 816-1 to 816-7. Sled spaces 803-1 to803-7 include respective primary regions 803-1A to 803-7A and respectiveexpansion regions 803-1B to 803-7B. With respect to each such sledspace, when the corresponding MPCM is coupled with a counterpart MPCM ofan inserted sled, the primary region may generally constitute a regionof the sled space that physically accommodates the inserted sled. Theexpansion region may generally constitute a region of the sled spacethat can physically accommodate an expansion module, such as expansionsled 718 of FIG. 7, in the event that the inserted sled is configuredwith such a module.

FIG. 9 illustrates an example of a rack 902 that may be representativeof a rack implemented according to rack architecture 800 of FIG. 8according to some embodiments. In the particular non-limiting exampledepicted in FIG. 9, rack 902 features seven sled spaces 903-1 to 903-7,which include respective primary regions 903-1A to 903-7A and respectiveexpansion regions 903-1B to 903-7B. In various embodiments, temperaturecontrol in rack 902 may be implemented using an air cooling system. Forexample, as reflected in FIG. 9, rack 902 may feature a plurality offans 919 that are generally arranged to provide air cooling within thevarious sled spaces 903-1 to 903-7. In some embodiments, the height ofthe sled space is greater than the conventional “1U” server height. Insuch embodiments, fans 919 may generally comprise relatively slow, largediameter cooling fans as compared to fans used in conventional rackconfigurations. Running larger diameter cooling fans at lower speeds mayincrease fan lifetime relative to smaller diameter cooling fans runningat higher speeds while still providing the same amount of cooling. Thesleds are physically shallower than conventional rack dimensions.Further, components are arranged on each sled to reduce thermalshadowing (i.e., not arranged serially in the direction of air flow). Asa result, the wider, shallower sleds allow for an increase in deviceperformance because the devices can be operated at a higher thermalenvelope (e.g., 250W) due to improved cooling (i.e., no thermalshadowing, more space between devices, more room for larger heat sinks,etc.).

MPCMs 916-1 to 916-7 may be configured to provide inserted sleds withaccess to power sourced by respective power modules 920-1 to 920-7, eachof which may draw power from an external power source 921. In variousembodiments, external power source 921 may deliver alternating current(AC) power to rack 902, and power modules 920-1 to 920-7 may beconfigured to convert such AC power to direct current (DC) power to besourced to inserted sleds. In some embodiments, for example, powermodules 920-1 to 920-7 may be configured to convert 277-volt AC powerinto 12-volt DC power for provision to inserted sleds via respectiveMPCMs 916-1 to 916-7. The embodiments are not limited to this example.

MPCMs 916-1 to 916-7 may also be arranged to provide inserted sleds withoptical signaling connectivity to a dual-mode optical switchinginfrastructure 914, which may be the same as—or similar to—dual-modeoptical switching infrastructure 514 of FIG. 5. In various embodiments,optical connectors contained in MPCMs 916-1 to 916-7 may be designed tocouple with counterpart optical connectors contained in MPCMs ofinserted sleds to provide such sleds with optical signaling connectivityto dual-mode optical switching infrastructure 914 via respective lengthsof optical cabling 922-1 to 922-7. In some embodiments, each such lengthof optical cabling may extend from its corresponding MPCM to an opticalinterconnect loom 923 that is external to the sled spaces of rack 902.In various embodiments, optical interconnect loom 923 may be arranged topass through a support post or other type of load-bearing element ofrack 902. The embodiments are not limited in this context. Becauseinserted sleds connect to an optical switching infrastructure via MPCMs,the resources typically spent in manually configuring the rack cablingto accommodate a newly inserted sled can be saved.

FIG. 10 illustrates an example of a sled 1004 that may be representativeof a sled designed for use in conjunction with rack 902 of FIG. 9according to some embodiments. Sled 1004 may feature an MPCM 1016 thatcomprises an optical connector 1016A and a power connector 1016B, andthat is designed to couple with a counterpart MPCM of a sled space inconjunction with insertion of MPCM 1016 into that sled space. CouplingMPCM 1016 with such a counterpart MPCM may cause power connector 1016 tocouple with a power connector comprised in the counterpart MPCM. Thismay generally enable physical resources 1005 of sled 1004 to sourcepower from an external source, via power connector 1016 and powertransmission media 1024 that conductively couples power connector 1016to physical resources 1005.

Sled 1004 may also include dual-mode optical network interface circuitry1026. Dual-mode optical network interface circuitry 1026 may generallycomprise circuitry that is capable of communicating over opticalsignaling media according to each of multiple link-layer protocolssupported by dual-mode optical switching infrastructure 914 of FIG. 9.In some embodiments, dual-mode optical network interface circuitry 1026may be capable both of Ethernet protocol communications and ofcommunications according to a second, high-performance protocol. Invarious embodiments, dual-mode optical network interface circuitry 1026may include one or more optical transceiver modules 1027, each of whichmay be capable of transmitting and receiving optical signals over eachof one or more optical channels. The embodiments are not limited in thiscontext.

Coupling MPCM 1016 with a counterpart MPCM of a sled space in a givenrack may cause optical connector 1016A to couple with an opticalconnector comprised in the counterpart MPCM. This may generallyestablish optical connectivity between optical cabling of the sled anddual-mode optical network interface circuitry 1026, via each of a set ofoptical channels 1025. Dual-mode optical network interface circuitry1026 may communicate with the physical resources 1005 of sled 1004 viaelectrical signaling media 1028. In addition to the dimensions of thesleds and arrangement of components on the sleds to provide improvedcooling and enable operation at a relatively higher thermal envelope(e.g., 250 W), as described above with reference to FIG. 9, in someembodiments, a sled may include one or more additional features tofacilitate air cooling, such as a heatpipe and/or heat sinks arranged todissipate heat generated by physical resources 1005. It is worthy ofnote that although the example sled 1004 depicted in FIG. 10 does notfeature an expansion connector, any given sled that features the designelements of sled 1004 may also feature an expansion connector accordingto some embodiments. The embodiments are not limited in this context.

FIG. 11 illustrates an example of a data center 1100 that may generallybe representative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. As reflectedin FIG. 11, a physical infrastructure management framework 1150A may beimplemented to facilitate management of a physical infrastructure 1100Aof data center 1100. In various embodiments, one function of physicalinfrastructure management framework 1150A may be to manage automatedmaintenance functions within data center 1100, such as the use ofrobotic maintenance equipment to service computing equipment withinphysical infrastructure 1100A. In some embodiments, physicalinfrastructure 1100A may feature an advanced telemetry system thatperforms telemetry reporting that is sufficiently robust to supportremote automated management of physical infrastructure 1100A. In variousembodiments, telemetry information provided by such an advancedtelemetry system may support features such as failureprediction/prevention capabilities and capacity planning capabilities.In some embodiments, physical infrastructure management framework 1150Amay also be configured to manage authentication of physicalinfrastructure components using hardware attestation techniques. Forexample, robots may verify the authenticity of components beforeinstallation by analyzing information collected from a radio frequencyidentification (RFID) tag associated with each component to beinstalled. The embodiments are not limited in this context.

As shown in FIG. 11, the physical infrastructure 1100A of data center1100 may comprise an optical fabric 1112, which may include a dual-modeoptical switching infrastructure 1114. Optical fabric 1112 and dual-modeoptical switching infrastructure 1114 may be the same as—or similarto—optical fabric 412 of FIG. 4 and dual-mode optical switchinginfrastructure 514 of FIG. 5, respectively, and may providehigh-bandwidth, low-latency, multi-protocol connectivity among sleds ofdata center 1100. As discussed above, with reference to FIG. 1, invarious embodiments, the availability of such connectivity may make itfeasible to disaggregate and dynamically pool resources such asaccelerators, memory, and storage. In some embodiments, for example, oneor more pooled accelerator sleds 1130 may be included among the physicalinfrastructure 1100A of data center 1100, each of which may comprise apool of accelerator resources—such as co-processors and/or FPGAs, forexample—that is globally accessible to other sleds via optical fabric1112 and dual-mode optical switching infrastructure 1114.

In another example, in various embodiments, one or more pooled storagesleds 1132 may be included among the physical infrastructure 1100A ofdata center 1100, each of which may comprise a pool of storage resourcesthat is globally accessible to other sleds via optical fabric 1112 anddual-mode optical switching infrastructure 1114. In some embodiments,such pooled storage sleds 1132 may comprise pools of solid-state storagedevices such as solid-state drives (SSDs). In various embodiments, oneor more high-performance processing sleds 1134 may be included among thephysical infrastructure 1100A of data center 1100. In some embodiments,high-performance processing sleds 1134 may comprise pools ofhigh-performance processors, as well as cooling features that enhanceair cooling to yield a higher thermal envelope of up to 250 W or more.In various embodiments, any given high-performance processing sled 1134may feature an expansion connector 1117 that can accept a far memoryexpansion sled, such that the far memory that is locally available tothat high-performance processing sled 1134 is disaggregated from theprocessors and near memory comprised on that sled. In some embodiments,such a high-performance processing sled 1134 may be configured with farmemory using an expansion sled that comprises low-latency SSD storage.The optical infrastructure allows for compute resources on one sled toutilize remote accelerator/FPGA, memory, and/or SSD resources that aredisaggregated on a sled located on the same rack or any other rack inthe data center. The remote resources can be located one switch jumpaway or two-switch jumps away in the spine-leaf network architecturedescribed above with reference to FIG. 5. The embodiments are notlimited in this context.

In various embodiments, one or more layers of abstraction may be appliedto the physical resources of physical infrastructure 1100A in order todefine a virtual infrastructure, such as a software-definedinfrastructure 1100B. In some embodiments, virtual computing resources1136 of software-defined infrastructure 1100/B may be allocated tosupport the provision of cloud services 1140. In various embodiments,particular sets of virtual computing resources 1136 may be grouped forprovision to cloud services 1140 in the form of SDI services 1138.Examples of cloud services 1140 may include—without limitation—softwareas a service (SaaS) services 1142, platform as a service (PaaS) services1144, and infrastructure as a service (IaaS) services 1146.

In some embodiments, management of software-defined infrastructure 1100Bmay be conducted using a virtual infrastructure management framework1150B. In various embodiments, virtual infrastructure managementframework 1150B may be designed to implement workload fingerprintingtechniques and/or machine-learning techniques in conjunction withmanaging allocation of virtual computing resources 1136 and/or SDIservices 1138 to cloud services 1140. In some embodiments, virtualinfrastructure management framework 1150B may use/consult telemetry datain conjunction with performing such resource allocation. In variousembodiments, an application/service management framework 1150C may beimplemented in order to provide QoS management capabilities for cloudservices 1140. The embodiments are not limited in this context.

Referring now to FIG. 12, a system 1210 for managing disaggregatedaccelerator resources in a disaggregated architecture may be implementedin accordance with the data centers 100, 300, 400, 1100 described abovewith reference to FIGS. 1, 3, 4, and 11. In the illustrative embodiment,managing disaggregated accelerator resources means facilitatingapplication workload processing by receiving workload processingrequests from an application and distributing all or part of theworkload to accelerator devices. These accelerator devices are selectedbased on configuration and capacity to efficiently process anapplication workload (e.g., cryptographic operations, compressionoperations, image processing operations, packet inspection operations,etc.). The accelerator devices may be located on the same compute device(e.g., compute sled) that is executing the application and/or on one ormore remote compute devices (e.g., remote accelerator sleds) which maybe otherwise inaccessible to the application. The workload processingrequests are received and fulfilled by a pooled system management engine(PSME) that may be locally installed on the same host compute device asthe application requesting workload processing. The term “PSME” isnomenclature used by Intel Corporation and is used herein merely forconvenience. It should be understood that the PSME may be embodied asany sled-, rack-, or tray-level management engine. The out-of-boxfunctions of the PSME are extended to provide the disaggregatedaccelerator management capability described herein, such that theapplication can leverage accelerator devices that may be otherwiseinaccessible.

In the illustrative embodiment, the system 1210 includes an orchestratorserver 1216 in communication with compute sleds 1230, 1232 andaccelerator sleds 1260, 1262. In the illustrative embodiment, theorchestrator server 1216 is included within a compute sled 1218. One ormore of the sleds 1230, 1232, 1260, or 1262 may be grouped into amanaged node, such as by the orchestrator server 1216, to collectivelyperform a workload, such as an application. A managed node may beembodied as an assembly of resources (e.g., physical resources 206),such as compute resources (e.g., physical compute resources 205-4),memory resources (e.g., physical memory resources 205-3), storageresources (e.g., physical storage resources 205-1), or other resources(e.g., physical accelerator resources 205-2), from the same or differentsleds (e.g., the sleds 204-1, 204-2, 204-3, 204-4, etc.) or racks (e.g.,one or more of racks 302-1 through 302-32). Further, a managed node maybe established, defined, or “spun up” by the orchestrator server 1216 atthe time a workload is to be assigned to the managed node or at anyother time, and may exist regardless of whether any workloads arepresently assigned to the managed node. The system 1210 may be locatedin a data center and provide storage and compute services (e.g., cloudservices) to a client device 1214 that is in communication with thesystem 1210 through a network 1212. The orchestrator server 1216 maysupport a cloud operating environment, such as OpenStack, and managednodes established by the orchestrator server 1216 may execute one ormore applications or processes (i.e., workloads), such as in virtualmachines or containers, on behalf of a user of the client device 1214.In the illustrative embodiment, the compute sled 1230 executes aworkload 1234 (e.g., an application) with one or more processors 1250,and the compute sled 1232 executes another workload 1236 (e.g., anotherapplication) with one or more processors 1252. Further, one or more ofcompute sleds 1230 and 1232 may host a PSME 1270, configured to performthe disaggregated accelerator resource management functions describedabove. Additionally, the accelerator sled 1260 includes one or moreaccelerator devices 1264 (e.g., physical accelerator resources 205-2)and the accelerator sled 1266 also includes one or more acceleratordevices 1266 (e.g., physical accelerator resources 205-2).

As described in more detail herein, a sled (e.g., the compute sled 1230)equipped with a PSME 1270 may detect accelerator devices within the datacenter (e.g., the system 1210), including discovering information abouteach detected accelerator device (e.g., processing power, configuration,specialized functionality, average utilization, or the like), receiverequests from the application for assistance in accelerating theexecution of application, and based on the discovery process and ananalysis of the request from the application, the sled, using the PSME1270, may schedule one or more portions (e.g., tasks) of the applicationto be accelerated by a corresponding accelerator device available in thesystem 1210 that is suited to accelerating the task (e.g., scheduling acryptography-related task on an accelerator device that includesspecialized circuitry for performing cryptographic operations). Further,in the illustrative embodiment, the PSME 1270 performs the abovefunctions out-of-band (e.g., without consuming compute capacity of thesled that would otherwise be used to execute the application and/or anunderlying operating system).

Referring now to FIG. 13, the compute sled 1230 may be embodied as anytype of compute device capable of performing the functions describedherein, including executing a workload (e.g., the workload 1234),obtaining a request from the workload 1234 to accelerate the executionof the workload, identifying the accelerator devices available in thesystem 1210 (e.g., on the compute sled 1230 and/or in other sleds 1232,1260, 1262) within the system 1210, including their features (e.g.,types of functions each accelerator device is able to accelerate) andavailability (e.g., present load), and selecting one or more of theaccelerator devices to execute one or more portions (e.g., tasks) of theworkload to increase the speed of execution of the workload. In theillustrative embodiment, the compute sled 1230 performs the abovefunctions without consuming compute capacity that would otherwise beused by the application (e.g., the workload) and/or an operating systemsupporting the application.

As shown in FIG. 13, the illustrative compute sled 1230 includes acompute engine 1302, an input/output (I/O) subsystem 1308, communicationcircuitry 1310, and one or more data storage devices 1314. The computesled 1230 may also include one or more accelerators, depicted asaccelerators 1320 and 1322. Of course, in other embodiments, the computesled 1230 may include other or additional components, such as thosecommonly found in a computer (e.g., display, peripheral devices, etc.).Additionally, in some embodiments, one or more of the illustrativecomponents may be incorporated in, or otherwise form a portion of,another component.

The compute engine 1302 may be embodied as any type of device orcollection of devices capable of performing various compute functionsdescribed below. In some embodiments, the compute engine 1302 may beembodied as a single device such as an integrated circuit, an embeddedsystem, a field-programmable gate array (FPGA), a system-on-a-chip(SOC), or other integrated system or device. Additionally, in someembodiments, the compute engine 1302 includes or is embodied as aprocessor 1304 (e.g., similar to the processor(s) 1250) and a memory1306. The processor 1304 may be embodied as any type of processorcapable of executing a workload (e.g., the application 1234). Forexample, the processor 1304 may be embodied as a single or multi-coreprocessor(s), a microcontroller, or other processor orprocessing/controlling circuit. In some embodiments, the processor 1304may be embodied as, include, or be coupled to an FPGA, an applicationspecific integrated circuit (ASIC), reconfigurable hardware or hardwarecircuitry, or other specialized hardware to facilitate performance ofthe functions described herein. The PSME 1270 may, in some embodiments,be included within a dedicated processor 1305 that is separate from theprocessor 1304 that performs other computing functions of the computeengine 1302 (e.g., executing applications). The PSME 1270 may beembodied as a specialized device, such as a co-processor, an FPGA, agraphics processing unit (GPU), or an ASIC, for performing theaccelerator resource management operations described above.

As described in more detail herein, the PSME 1270 is configured tomanage disaggregated accelerator resources (e.g., by responding toworkload processing requests with an accelerator service request to, forexample, the orchestrator 1216 for accelerator services from remoteaccelerators, such as on accelerator sleds 1260, 1262). In addition, andas described above, the compute sled 1230 includes accelerators 1320 and1322 that may be configured to perform acceleration tasks (e.g.,cryptographic operations on an accelerator specially configured toperform cryptographic operations). The PSME 1270 is configured to directall or part of a workload from a workload processing request toaccelerator 1320 and/or accelerator 1322 (i.e., accelerators hosted bythe same compute sled as that running the application and the PSME 1270)in an out-of-band capacity (e.g., without consuming compute capacity ofthe sled that would otherwise be used to execute the application and/oran underlying operating system). In some embodiments, the PSME 1270 isconfigured to direct all or part of a workload from a workloadprocessing request to accelerator 1320 and/or accelerator 1322 in anin-band capacity as well if secure and authenticated channels are used(e.g., by consuming compute capacity of the sled that would otherwise beused to execute the application and/or an underlying operating system).

The main memory 1306 may be embodied as any type of volatile (e.g.,dynamic random access memory (DRAM), etc.) or non-volatile memory ordata storage capable of performing the functions described herein.Volatile memory may be a storage medium that requires power to maintainthe state of data stored by the medium. Non-limiting examples ofvolatile memory may include various types of random access memory (RAM),such as dynamic random access memory (DRAM) or static random accessmemory (SRAM). One particular type of DRAM that may be used in a memorymodule is synchronous dynamic random access memory (SDRAM). Inparticular embodiments, DRAM of a memory component may comply with astandard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2Ffor DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM,JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 forLPDDR3, and JESD209-4 for LPDDR4 (these standards are available atwww.jedec.org). Such standards (and similar standards) may be referredto as DDR-based standards and communication interfaces of the storagedevices that implement such standards may be referred to as DDR-basedinterfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A memory devicemay also include future generation nonvolatile devices, such as a threedimensional crosspoint memory device, or other byte addressablewrite-in-place nonvolatile memory devices. In one embodiment, the memorydevice may be or may include memory devices that use chalcogenide glass,multi-threshold level NAND flash memory, NOR flash memory, single ormulti-level Phase Change Memory (PCM), a resistive memory, nanowirememory, ferroelectric transistor random access memory (FeTRAM),anti-ferroelectric memory, magnetoresistive random access memory (MRAM)memory that incorporates memristor technology, resistive memoryincluding the metal oxide base, the oxygen vacancy base and theconductive bridge Random Access Memory (CB-RAM), or spin transfer torque(STT)-MRAM, a spintronic magnetic junction memory based device, amagnetic tunneling junction (MTJ) based device, a DW (Domain Wall) andSOT (Spin Orbit Transfer) based device, a thyristor based memory device,or a combination of any of the above, or other memory. The memory devicemay refer to the die itself and/or to a packaged memory product.

In some embodiments, 3D crosspoint memory may comprise a transistor-lessstackable cross point architecture in which memory cells sit at theintersection of word lines and bit lines and are individuallyaddressable and in which bit storage is based on a change in bulkresistance. In some embodiments, all or a portion of the main memory1306 may be integrated into the processor 1304. In operation, the mainmemory 1306 may store various software and data used during operationsuch as accelerator configuration data, accelerator directory data,application data, applications, programs, libraries, and drivers.

The compute engine 1302 is communicatively coupled to other componentsof the compute sled 1230 via the I/O subsystem 1308, which may beembodied as circuitry and/or components to facilitate input/outputoperations with the compute engine 1302 (e.g., with the processor 1304and/or the main memory 1306) and other components of the compute sled1230. For example, the I/O subsystem 1308 may be embodied as, orotherwise include, memory controller hubs, input/output control hubs,integrated sensor hubs, firmware devices, communication links (e.g.,point-to-point links, bus links, wires, cables, light guides, printedcircuit board traces, etc.), and/or other components and subsystems tofacilitate the input/output operations. In some embodiments, the I/Osubsystem 1308 may form a portion of a system-on-a-chip (SoC) and beincorporated, along with one or more of the processor 1304, the mainmemory 1306, and other components of the compute sled 1230, into thecompute engine 1302.

The communication circuitry 1310 may be embodied as any communicationcircuit, device, or collection thereof, capable of enablingcommunications over the network 1212 between the compute sled 1230 andanother compute device (e.g., the orchestrator server 1216, and/or oneor more sleds 1230, 1232, 1260, 1262). The communication circuitry 1310may be configured to use any one or more communication technology (e.g.,wired or wireless communications) and associated protocols (e.g.,Ethernet, Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication.

The illustrative communication circuitry 1310 includes a networkinterface controller (NIC) 1312. The NIC 1312 may be embodied as one ormore add-in-boards, daughter cards, network interface cards, controllerchips, chipsets, or other devices that may be used by the compute sled1230 to connect with another compute device (e.g., the orchestratorserver 1216 and/or the sleds 1232, 1260, 1262). In some embodiments, theNIC 1312 may be embodied as part of a system-on-a-chip (SoC) thatincludes one or more processors, or included on a multichip package thatalso contains one or more processors. In some embodiments, the NIC 1312may include a local processor (not shown) and/or a local memory (notshown) that are both local to the NIC 1312. In such embodiments, thelocal processor of the NIC 1312 may be capable of performing one or moreof the functions of the compute engine 1302 described herein.Additionally or alternatively, in such embodiments, the local memory ofthe NIC 1312 may be integrated into one or more components of thecompute sled 1230 at the board level, socket level, chip level, and/orother levels. In some embodiments, the PSME 1270 may be included in theNIC 1312.

The one or more illustrative data storage devices 1314, may be embodiedas any type of devices configured for short-term or long-term storage ofdata such as, for example, memory devices and circuits, memory cards,hard disk drives, solid-state drives, or other data storage devices.Each data storage device 1314 may include a system partition that storesdata and firmware code for the data storage device 1314. Each datastorage device 1314 may also include an operating system partition thatstores data files and executables for an operating system.

Additionally or alternatively, the compute sled 1230 may include one ormore peripheral devices 1316. Such peripheral devices 1316 may includeany type of peripheral device commonly found in a compute device such asa display, speakers, a mouse, a keyboard, and/or other input/outputdevices, interface devices, and/or other peripheral devices.

The client device 1214, the orchestrator server 1216, and the computesled 1232, may have components similar to those described in FIG. 13.The description of those components of the compute sled 1230 is equallyapplicable to the description of components of those devices and is notrepeated herein for clarity of the description, with the exception thatthe client device 1214 and the orchestrator server 1216 do not includethe PSME 1270 and, in the illustrative embodiment, may not includeaccelerators 1320, 1322. Further, it should be appreciated that any ofthe client device 1214, the orchestrator server 1216, and the computesleds 1230, 1232, may include other components, sub-components, anddevices commonly found in a computing device, which are not discussedabove in reference to the compute sled 1230 and not discussed herein forclarity of the description. In addition, the accelerator sleds 1260,1262 include components similar to those described above, and it shouldbe understood that the accelerator(s) 1264, 1266 shown in FIG. 12 may besimilar to the accelerators 1320, 1322 described above with reference toFIG. 13.

As described above, the compute sled 1230, the orchestrator server 1216,and the sleds 1230, 1232, 1260, 1262 are illustratively in communicationvia the network 1212, which may be embodied as any type of wired orwireless communication network, including global networks (e.g., theInternet), local area networks (LANs) or wide area networks (WANs),cellular networks (e.g., Global System for Mobile Communications (GSM),3G, Long Term Evolution (LTE), Worldwide Interoperability for MicrowaveAccess (WiMAX), etc.), digital subscriber line (DSL) networks, cablenetworks (e.g., coaxial networks, fiber networks, etc.), or anycombination thereof.

Referring now to FIG. 14, the compute sled 1230 may establish anenvironment 1400 during operation. The illustrative environment 1400includes a network communicator 1420, and a PSME manager 1430. Each ofthe components of the environment 1400 may be embodied as hardware,firmware, software, or a combination thereof. As such, in someembodiments, one or more of the components of the environment 1400 maybe embodied as circuitry or a collection of electrical devices (e.g.,network communicator circuitry 1420, PSME manager circuitry 1430, etc.).It should be appreciated that, in such embodiments, one or more of thenetwork communicator circuitry 1420 or the PSME manager circuitry 1430may form a portion of one or more of the compute engine 1302, the PSME1270, the communication circuitry 1310, the I/O subsystem 1308, and/orother components of the compute sled 1230.

Additionally, the illustrative environment 1400 includes acceleratorconfiguration data 1404 which may be embodied as any data indicative ofthe accelerator configuration, including accelerator processing speed,types of functions that each accelerator is capable of accelerating(e.g., cryptographic operations, compression operations, etc.), parallelprocessing capacity, specialized configuration modes, acceleratorarchitecture data (e.g., number of cores), associated sled identifier(e.g., associated accelerator sled identifiers), or the like.

Additionally, the illustrative environment 1400 includes acceleratordirectory data 1406, which may be embodied as any data indicative of adata structure holding lists of accelerator devices, accelerator devicetypes (e.g., FPGA, GPU, ASIC, or the like). In addition, the acceleratordirectory data 1406 may store accelerator device identifiers incorrelation with the corresponding accelerator sled identifiers. Theaccelerator directory 1406 may also include accelerator usage historydata (e.g., applications that most frequently used the accelerator,specialized usages or configurations for the accelerator, like graphicsprocessing or audio processing, or the like), accelerator performancemetrics, accelerator age data (e.g., how long the accelerator has beenconnected to the orchestrator server 1216, whether the accelerator is anewer or older accelerator, whether it was previously removed fromcommission) or the like.

Additionally, in the illustrative embodiment, the environment 1400includes application data 1408, which may be embodied as any dataindicative of applications requesting workload processing from the PSME1270. Application data 1408 may also be embodied as any data indicativeof application workload processing requests, schedules of workloadprocessing requests (e.g., repetitive requests), or the like.

In the illustrative environment 1400, the network communicator 1420,which may be embodied as hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof asdiscussed above, is configured to facilitate inbound and outboundnetwork communications (e.g., network traffic, network packets, networkflows, etc.) to and from the compute sled 1230, respectively. To do so,the network communicator 1420 is configured to receive and process datapackets from one system or computing device (e.g., a compute sled 1230,1232) and to prepare and send data packets to another computing deviceor system (e.g., an accelerator sled 1260, 1262). Accordingly, in someembodiments, at least a portion of the functionality of the networkcommunicator 1420 may be performed by the communication circuitry 1310,and, in the illustrative embodiment, by the NIC 1312.

The PSME manager 1430, which may be embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof, is configured to provide efficient disaggregatedaccelerator management across the system 1210. To do so, in theillustrative embodiment, the PSME manager 1430 includes a requestanalyzer interface 1432, an accelerator selection manager 1434, anaccelerator query manager 1436, and an accelerator directory manager1438. The request analyzer interface 1432, in the illustrativeembodiment, is configured to process application workload processingrequests (e.g., from a compute sled 1230) by receiving a workloadprocessing request (e.g., one that originates from the application1234), determining whether to analyze the workload processing request,analyzing the workload processing request for request metadata, such asone or more request parameters, identifying each request parametertransmitted with the workload processing request, and identifying theworkload submitted by the application. In some embodiments, the requestanalyzer interface 1442 may generate an accelerator service request thatis specifically formatted for consumption by the orchestrator server1216. The accelerator service request is configured to include the oneor more request parameters, the workload transmitted by the application,and the selected accelerators (e.g., the accelerator 1264) along withassociated accelerator sled identifiers (e.g., for the accelerator sled1260).

The accelerator selection manager 1434, in the illustrative embodiment,is configured to query the accelerator directory (e.g., as representedby the accelerator directory data 1406) in order to identify acceleratordevices that would be best suited to process the workload as defined bythe request parameters extracted from the workload processing request.More specifically, and as indicated in the illustrative embodiment, theaccelerator selection manager 1434 is configured to query theaccelerator directory using the request parameters in order to locateaccelerator devices that have a configuration matching the requestparameters. As described in more detail herein, the query performed bythe accelerator selection manager 1434 will return search resultsincluding identifiers for one or more accelerators whose configurationis a match for one or more request parameters. Based on the returnedresults, the accelerator selection manager 1434 is configured to collecta set of one or more accelerator device identifiers that is thenincluded within the accelerator service request sent to the orchestratorserver 1216 as described above.

The accelerator query manager 1436, in the illustrative embodiment, isconfigured to query an orchestrator (e.g., the orchestrator server 1216)for updated information regarding accelerator devices that areaccessible to or in communication with the orchestrator server 1216, tomaintain and keep current the accelerator directory (e.g., asrepresented by accelerator directory data 1406 or collectively byaccelerator directory data 1406 and accelerator configuration data1404). Accordingly, the accelerator query manager 1436 periodically (oron demand) transmits environment discovery queries to the orchestratorserver 1216 for accelerator updates. Accelerator updates may includelists of accelerator identifiers for newly connected accelerators,removed accelerators, or the like. The accelerator query manager 1436will also transmit accelerator configuration update queries to theorchestrator server 1216. The accelerator configuration update queriesmay request the orchestrator server 1216 to return acceleratorconfiguration data (e.g., to review any recent accelerator configurationchanges) for each identified accelerator. The accelerator query manager1436 is also configured to transmit accelerator health queries to theorchestrator server 1216. The accelerator health queries, in theillustrative embodiment, request data regarding accelerator healthmetrics, including accelerator uptime statistics, accelerator downtimestatistics, time elapsed since accelerator start, time elapsed sinceaccelerator reconfiguration, accelerator error statistics, or the like.

The accelerator directory manager 1438, in the illustrative embodiment,is configured to maintain a current and continuously updated directoryof all accelerators that are in communication with the orchestratorserver 1216. In the illustrative embodiment, the accelerator directorymanager 1438 receives results of the queries transmitted by theaccelerator query manager 1436. The accelerator directory manager 1438uses the query results to update the accelerator directory (e.g., asembodied by the accelerator directory data 1406).

It should be appreciated that each of the request analyzer interface1432, the accelerator selection manager 1434, the accelerator querymanager 1436, and the accelerator directory manager 1438 may beseparately embodied as hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof. Forexample, the request analyzer interface 1432 may be embodied as ahardware component, while the accelerator selection manager 1434, theaccelerator query manager 1436, and the accelerator directory manager1438 are embodied as virtualized hardware components or as some othercombination of hardware, firmware, software, virtualized hardware,emulated architecture, and/or a combination thereof. Further it shouldbe appreciated that in some embodiments, a sled 1230, 1232, 1260, 1262containing the PSME 1270 may establish an environment similar to theenvironment 1400 described above.

Referring now to FIG. 15, in use, a compute device (e.g., the computesled 1230 including the PSME 1270) may execute a method 1500 formanaging disaggregated accelerator device resources. For simplicity, themethod 1500 is described below as being performed by the PSME 1270.However, it should be understood that in other embodiments, the method1500 may be performed by one or more other compute devices (e.g., a sled1232, 1260, 1262 including the PSME 1270). The method 1500 begins withblock 1501, in which the PSME 1270 receives a workload processingrequest from an application. In embodiments, the compute sled 1230 (orsome sub-component of the compute sled 1230) may receive and process theworkload processing request from the application. In the illustrativeembodiment, the PSME 1270 receives the workload processing request froman application and processes the workload processing request in anout-of-band capacity (e.g., without use of the host compute sled'sprocessor or operating system). As described above, in some embodimentsthe PSME 1270 may direct all or part of a workload from a workloadprocessing request to accelerator 1320 and/or accelerator 1322 in anin-band capacity, with secure and authenticated channels (e.g., byconsuming compute capacity of the sled that would otherwise be used toexecute the application and/or an underlying operating system).

More specifically, the PSME 1270 receives the processing request from anapplication that is executing on the same compute sled as the PSME. Inother embodiments, the PSME 1270 may receive workload processingrequests from applications executing on other compute sleds (e.g., viathe orchestrator server 1216). The method 1500 advances to block 1502.

In block 1502, if the PSME 1270 determines whether to analyze theworkload processing request, the method 1500 advances to block 1504.Otherwise the method returns to block 1501. In block 1504, the PSME 1270analyzes the workload processing request. In the illustrativeembodiment, the workload processing request will include a definedworkload (e.g., data that is to be processed by one or more acceleratordevices). In addition, and as illustrated in block 1506, the PSME 1270retrieves request metadata from the workload processing request. In theillustrative embodiment, the PSME 1270 is configured to parse theworkload processing request in order to identify request metadata thatfurther includes one or more request parameters. More specifically, andas illustrated in block 1508, the PSME 1270 determines any service levelagreement (SLA) requirements included within the workload processingrequest. For example, the request parameters may include specificworkload processing requirements such as a maximum processing time(e.g., a target latency), a minimum throughput requirement (e.g., atarget throughput), a threshold level of logical integrity requiredduring processing, a predefined error-checking mechanism or error rateenvelope, or the like. The metadata may additionally or alternativelyindicate whether two or more portions of the workload may be acceleratedconcurrently (e.g., in parallel).

The method 1500 advances to block 1510, in which the PSME 1270 queriesan accelerator directory (e.g., the accelerator directory 1406) todetermine the accelerator device(s) that would be best fit for theworkload processing request. In the illustrative embodiment, the querymay, for example, use one of the request parameters as a key. Morespecifically, the PSME 1270 may query the accelerator directory for allaccelerator devices that can satisfy a certain request parameter (e.g.,a specific SLA requirement such as completion of processing within amaximum processing time). For example, and as indicated in block 1512,the PSME 1270 retrieves an accelerator instance from the directory usingan accelerator identifier. The accelerator identifier may be part of aset of search results generated as a result of querying the acceleratordirectory. For example, and as indicated in block 1514, the PSME 1270reviews the accelerator configuration. As described earlier with respectto accelerator configuration data 1404, the accelerator configurationmay include data such as accelerator processing speed, types offunctions that the accelerator is capable of accelerating, parallelprocessing capacity, specialized configuration modes, acceleratorarchitecture data (e.g., number of cores), associated sled identified(e.g., associated accelerator sled identifiers), or the like.

The method 1500 advances to block 1516, in which the PSME 1270 matchesrequest metadata to the accelerator based on the acceleratorconfiguration. For example, the PSME 1270 may determine that theidentified accelerator instance is capable of satisfying each requestparameter in the workload processing request. As another example, thePSME 1270 may determine that the single accelerator instance cannotsatisfy each request parameter. Accordingly, the PSME 1270 will retrieveanother accelerator instance from the accelerator directory (e.g., byre-executing the query as described with respect to block 1510. As aresult, the PSME 1270 determines a combination of accelerator resourcesthat can together satisfy all of the request parameters in the workloadprocessing request.

Referring now to FIG. 16, in use, the method 1500 advances to block1518, in which the PSME 1270 determines whether the accelerator oraccelerators retrieved as a result of the query in block 1510 are ableto satisfy all request parameters. If the retrieved accelerator oraccelerators are not able to satisfy all request parameters, the method1500 returns to block 1510 to continue to query the acceleratordirectory for accelerators that satisfy all request parameters. In someembodiments, rather than locally determining the accelerator(s) capableof satisfying the request, the compute sled 1230 may send the requestwith the request parameter(s) to the orchestrator server 1216 andreceive, in response, an identification of the accelerator(s) that arecapable of processing the workload in accordance with the requestparameter(s). The method 1500 advances to block 1520. In theillustrative embodiment, the PSME 1270 determines to transmit theworkload from the workload processing request to the selectedaccelerator(s). The method 1500 advances to block 1522, in which thePSME 1270 retrieves, from the accelerator directory, the acceleratorsled identifier for the selected accelerator(s).

Using the accelerator sled identifier(s), the method 1500 advances toblock 1524. The compute sled 1230 is configured to transmit outgoingmessages from the orchestrator server 1216. In the illustrativeembodiment, the PSME 1270 provides the accelerator sled identifier(s)for the selected accelerator(s) to the orchestrator server 1216 (e.g.,in an accelerator service request). The method 1500 advances to block1526. The compute sled 1230 is configured to receive incoming messagesfrom the orchestrator server 1216. In the illustrative embodiment, thePSME 1270 receives an approval from the orchestrator server 1216representing that the orchestrator server 1216 approves the transmissionof the workload from the PSME 1270 to the one or more acceleratorsidentified to process the workload. As described earlier, theaccelerators may be hosted by accelerator sleds that are not hosts ofthe PSME 1270. In a related embodiment, the accelerator(s) may be localto the compute sled 1230 that also hosts the PSME 1270. The method 1500advances to block 1528, in which the PSME 1270 transmits the workloadfrom the application to the orchestrator. In the illustrative embodiment(not shown), the identified accelerators will process the workload asprovided from the PSME 1270. The accelerators will complete processingto generate a work product. In the illustrative embodiment, this workproduct could take the form of processed data. For example, theprocessed data may be encrypted data, decrypted data, compressed data,decompressed data, search function results, processed audio or videodata, or the like. The work product may also be message codes ornotifications. For example, the work product may be a notification thatthe provided workload resulted in a certain audio or visual state on anaudio or visual display device, a specific network state, confirmationof a remote wireless or wired communication, a receipt or transmissionof a signal, a test result, or the like.

The method 1500 advances to block 1530, in which the PSME 1270 receivesthe work product back from the orchestrator server 1216. Morespecifically, the identified accelerator(s) will process the providedworkload and return the resulting work product to the orchestratorserver 1216. The orchestrator server 1216 then transmits the workproduct to the PSME 1270. The method 1500 advances to block 1532, inwhich the PSME 1270 sends the work product to the application.

Referring now to FIG. 17, in use, a compute device (e.g., the computesled 1230 and/or another sled 1232, 1260, 1262 including the PSME 1270)may execute a method 1700 for managing disaggregated accelerator deviceresources. More specifically, the method 1700 pertains to querying anorchestrator (e.g., the orchestrator server 1216) for updatedinformation regarding accelerator devices that are accessible to or incommunication with the orchestrator server 1216. The objective is tomaintain and keep current the accelerator directory (e.g., asrepresented by accelerator directory data 1406 or collectively byaccelerator directory data 1406 and accelerator configuration data1404). The method 1700 begins at block 1702, in which the PSME 1270determines whether to query orchestrator for accelerator updates. Forexample, and in the illustrative embodiment, the PSME 1270 may have aregularly scheduled process to query the orchestrator server 1216 foraccelerator updates. As another example, the PSME 1270 is configured toquery the orchestrator server 1216 on demand by an operator (e.g., ahuman supervisor of the compute sled 1230).

The method 1700 advances to block 1704, in which the PSME 1270 queriesthe orchestrator server 1216 for accelerator updates. For example, andas indicated in block 1706, the PSME 1270 queries for any newaccelerators that have entered into communication with the orchestratorserver 1216. In a related embodiment, the PSME 1270 may query theorchestrator server 1216 for accelerator identifiers for allaccelerators connected to the orchestrator server 1216. The PSME 1270will then compare the list of accelerators returned by the orchestratorserver 1216 to the accelerator directory maintained by the PSME 1270.The PSME 1270 uses the comparison to identify new accelerators.Additionally, and as indicated in block 1708, the PSME 1270 queries theorchestrator server 1216 for removed accelerators. Similar to block1706, the orchestrator may return a list of accelerator identifiers forremoved accelerators that the PSME 1270 then uses to remove acceleratorentries in its accelerator directory. In a related embodiment, the PSME1270 may query the orchestrator server 1216 for all acceleratorsconnected to the orchestrator server 1216. The PSME 1270 will thencompare the list of accelerators returned by the orchestrator server1216 to the accelerator directory maintained by the PSME 1270. The PSME1270 uses the comparison to identify removed accelerators.

Additionally, and as indicated in block 1710, the PSME 1270 willdetermine whether an accelerator configuration has changed for anyaccelerator in the accelerator directory. More specifically, the PSME1270 queries the orchestrator server 1216 for accelerator configurationdata in addition to accelerator identifiers as described above withrespect to blocks 1706 and 1708. For example, the PSME 1270 willtransmit one or more accelerator identifiers along with a request foraccelerator configuration data for the identified accelerators. Themethod 1700 advances to block 1712, in which the PSME 1270 receivesaccelerator updates from the orchestrator. In other words, theorchestrator server 1216 will return accelerator configuration data inresponse to the query. Using the returned configuration data, the PSME1270 will determine whether to update the accelerator configuration datastored within the accelerator directory for the identified accelerator.For example, and as indicated in block 1714, the PSME 1270 updates theaccelerator directory entry for the identified accelerator in responseto a notification from the orchestrator server 1216 of a changedconfiguration for the identified accelerator.

Referring now to FIG. 18, in use, the, a compute device (e.g., thecompute sled 1230 and/or another sled 1232, 1260, 1262 including thePSME 1270) may execute a method 1800 for managing disaggregatedaccelerator device resources. More specifically, the method 1800pertains to querying an orchestrator (e.g., the orchestrator server1216) for health data (or status data) regarding accelerator devicesthat are accessible to or in communication with the orchestrator server1216.

The method 1800 begins at block 1802, in which the PSME 1270 determinesto query the orchestrator server 1216 for one or more accelerator healthmetrics. The method 1800 advances to block 1804, in which the PSME 1270queries the orchestrator server 1216 using a particular acceleratoridentifier (e.g., an accelerator identifier for one of the accelerators1264). As indicated in block 1806, the PSME 1270 may receive, from theorchestrator server 1216, an accelerator health metric. As used herein,an accelerator health metric may include one or more of acceleratoruptime statistics, accelerator downtime statistics, time elapsed sinceaccelerator start, time elapsed since accelerator reconfiguration,accelerator error statistics, or the like. Additionally, and asindicated in block 1808, the PSME 1270 may receive an acceleratorthroughput metric. As used herein, an accelerator throughput metric mayinclude or more of a present accelerator processing speed, anaccelerator processing speed history, or the like. Additionally, and asindicated in block 1810, the PSME 1270 may receive an acceleratoroperational status. As used herein, an accelerator operational statusmay include one or more of an accelerator temperature, net acceleratorprocessing load, or the like.

The method 1800 advances to block 1812, in which the PSME 1270determines whether the accelerator is presently satisfying (or isprojected to satisfy) one or more request parameters as defined by theworkload processing request (e.g., the workload processing requestdescribed above with respect to FIGS. 15 and 16). For example, the PSME1270 may determine that an identified accelerator is not satisfying athroughput requirement (or the accelerator's defined portion of thethroughput requirement) as defined by the request parameters of theworkload processing request. Based on this determination, the method1800 advances to block 1814, in which the PSME 1270 queries theaccelerator directory for a replacement accelerator. More specifically,the PSME 1270 queries the accelerator directory using the acceleratorconfiguration data of the previously identified accelerator (or failingaccelerator) that is no longer satisfying one or more requestparameters. In the illustrative embodiment, querying the acceleratordirectory using the abovementioned accelerator configuration datareturns at least one result in the form of a replacement accelerator.The identified replacement accelerator is capable of performing theworkload that was being performed by the failing accelerator that is nolonger satisfying request parameters. The method 1800 advances to block1816, in which the PSME 1270 routes the workload away from the failingaccelerator and toward the replacement accelerator. More specifically,the PSME 1270 performs the method blocks 1522 to 1532 of FIG. 16 usingthe accelerator identifier in order to have the replacement acceleratorprocess the workload. The method 1800 advances to block 1818, in whichthe PSME 1270 determines whether the orchestrator server 1216 is to bequeried regarding the health of more accelerators. For example, the PSME1270 may have a scheduled task to periodically check for acceleratorhealth during the time an accelerator is processing a workload assignedby the PSME 1270.

The method 1800 advances to block 1820, in which the PSME 1270determines whether the orchestrator server 1216 should be queried formore accelerator health metrics. If the PSME 1270 determines that healthdata is required for more accelerators, the method 1800 returns to block1804. If the PSME 1270 determines that no more accelerator health datais required for the present time period, the method 1800 advances toblock 1822, in which the PSME 1270 updates the accelerator directory atthe PSME 1270 with the updated accelerator health data as provided bythe orchestrator server 1216.

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes a compute device to manage workflow to disaggregatedcomputing resources, the compute device comprising a compute engine toreceive a workload processing request, the workload processing requestdefined by at least one request parameter; determine at least oneaccelerator device capable of processing a workload in accordance withthe at least one request parameter; transmit a workload to the at leastone accelerator device; receive a work product produced by the at leastone accelerator device from the workload; and provide the work productto an application.

Example 2 includes the subject matter of Example 1, and wherein thecompute engine comprises a pooled system management engine (PSME),wherein the PSME operates in an out-of-band capacity with respect to thecompute device, and wherein to receive the workload processing requestcomprises to receive the workload processing request without utilizing ahost processor and without utilizing a host operating system of thecompute device, to determine the at least one accelerator device capableof processing the workload comprises to determine the at least oneaccelerator device without utilizing the host processor and withoututilizing the host operating system of the compute device, to transmitthe workload to the at least one accelerator device comprises totransmit the workload without utilizing the host processor and withoututilizing the host operating system of the compute device, to receivethe work product produced by the at least one accelerator devicecomprises to receive the work product without utilizing the hostprocessor and without utilizing the host operating system of the computedevice, and to provide the work product to the application comprises toprovide the work product to the application without utilizing the hostprocessor and without utilizing the host operating system of the computedevice.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein to determine the at least one accelerator device capable ofprocessing the workload comprises to determine at least one otheraccelerator device, the at least one other accelerator device beinghosted on the compute device that also hosts the PSME.

Example 4 includes the subject matter of any of Examples 1-3, andwherein to receive the workload processing request comprises to receivethe workload processing request from an application executed on thecompute device.

Example 5 includes the subject matter of any of Examples 1-4, andwherein the plurality of instructions, when executed by the one or moreprocessors, further cause the compute device to parse the workloadprocessing request for the at least one request parameter, wherein theat least one request parameter corresponds to a service-level agreement(SLA) requirement.

Example 6 includes the subject matter of any of Examples 1-5, andwherein the compute engine is further to generate an accelerator devicedirectory, wherein the accelerator device directory stores anaccelerator device identifier identifying the at least one acceleratordevice in correlation with configuration data and an accelerator sledidentifier for the at least one accelerator device, and wherein theconfiguration data is indicative of a number of operations per secondthat the at least one accelerator device is capable of performing, afunction that the at least one accelerator device is capable ofaccelerating, and a present utilization of the at least one acceleratordevice.

Example 7 includes the subject matter of any of Examples 1-6, andwherein the compute engine is further to identify a configurationparameter of the at least one accelerator device from the acceleratordevice directory; and determine that the configuration parameterrepresents a capability of the at least one accelerator device toprocess the workload.

Example 8 includes the subject matter of any of Examples 1-7, andwherein the compute engine is further to retrieve an accelerator sledidentifier for the at least one accelerator device; and transmit anaccelerator device request to an orchestrator, wherein the acceleratordevice request includes a request to transmit the workload to the atleast one accelerator device associated with the accelerator sledidentifier.

Example 9 includes the subject matter of any of Examples 1-8, andwherein the compute engine is further to receive the work product fromthe orchestrator, the work product representing a completion ofprocessing of the workload by the at least one accelerator device.

Example 10 includes the subject matter of any of Examples 1-9, andwherein to transmit a workload to the at least one accelerator devicecomprises to transmit the workload in-band, through a secure andauthenticated channel.

Example 11 includes the subject matter of any of Examples 1-10, andwherein to determine at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises to determine, based on an accelerator devicedirectory, the at least one accelerator device capable of processing aworkload in accordance with the at least one request parameter.

Example 12 includes the subject matter of any of Examples 1-11, andwherein the determined accelerator device is hosted on a remote devicedifferent from the compute device.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to determine the at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises to send the workload processing request to anorchestrator server; and receive, from the orchestrator server, anidentification of the at least one accelerator device capable ofprocessing the workload.

Example 14 includes the subject matter of any of Examples 1-13, andwherein the at least one request parameter includes metadata indicativeof whether two or more portions of the workload can be acceleratedconcurrently.

Example 15 includes the subject matter of any of Examples 1-14, andwherein the at least one request parameter includes metadata indicativeof a target quality of service associated with the workload.

Example 16 includes the subject matter of any of Examples 1-15, andwherein the metadata is indicative of at least one of a target latencyor a target throughput associated with the workload.

Example 17 includes a method for managing workflow to disaggregatedcomputing resources, the method comprising receiving, by a computedevice, a workload processing request, the workload processing requestdefined by at least one request parameter; determining, by the computedevice, at least one accelerator device capable of processing a workloadin accordance with the at least one request parameter; transmitting, bythe compute device, a workload to the at least one accelerator device;receiving, by the compute device, a work product produced by the atleast one accelerator device from the workload; and providing, by thecompute device, the work product to an application.

Example 18 includes the subject matter of Example 17, and wherein thecompute device includes a pooled system management engine (PSME),wherein the PSME operates in an out-of-band capacity with respect to thecompute device, and wherein receiving the workload processing requestcomprises receiving the workload processing request without utilizing ahost processor and without utilizing a host operating system of thecompute device, determining the at least one accelerator device capableof processing the workload comprises determining the at least oneaccelerator device without utilizing the host processor and withoututilizing the host operating system of the compute device, transmittingthe workload to the at least one accelerator device comprisestransmitting the workload without utilizing the host processor andwithout utilizing the host operating system of the compute device,receiving the work product produced by the at least one acceleratordevice comprises receiving the work product without utilizing the hostprocessor and without utilizing the host operating system of the computedevice, and providing the work product to the application comprisesproviding the work product to the application without utilizing the hostprocessor and without utilizing the host operating system of the computedevice.

Example 19 includes the subject matter of any of Examples 17 and 18, andwherein determining the at least one accelerator device capable ofprocessing the workload comprises determining at least one otheraccelerator device, the at least one other accelerator device beinghosted on the compute device that also hosts the PSME device.

Example 20 includes the subject matter of any of Examples 17-19, andwherein receiving the workload processing request comprises receivingthe workload processing request from an application executing on thecompute device.

Example 21 includes the subject matter of any of Examples 17-20, andfurther including parsing, by the compute device, the workloadprocessing request for the at least one request parameter, wherein theat least one request parameter corresponds to a service-level agreement(SLA) requirement.

Example 22 includes the subject matter of any of Examples 17-21, andfurther including generating an accelerator device directory, whereinthe accelerator device directory stores an accelerator device identifieridentifying the at least one accelerator device in correlation withconfiguration data and an accelerator sled identifier for the at leastone accelerator device, and wherein the configuration data is indicativeof a number of operations per second that the at least one acceleratordevice is capable of performing, a function that the at least oneaccelerator device is capable of accelerating, and a present utilizationof the at least one accelerator device.

Example 23 includes the subject matter of any of Examples 17-22, andfurther including identifying, by the compute device, a configurationparameter of the at least one accelerator device from the acceleratordevice directory; and determining, by the compute device, that theconfiguration parameter represents a capability of the at least oneaccelerator device to process the workload.

Example 24 includes the subject matter of any of Examples 17-23, andfurther including retrieving, by the compute device, an accelerator sledidentifier for the at least one accelerator device; and transmitting, bythe compute device, an accelerator device request to an orchestrator,wherein the accelerator device request includes a request to transmitthe workload to the at least one accelerator device associated with theaccelerator sled identifier.

Example 25 includes the subject matter of any of Examples 17-24, andfurther including receiving the work product from the orchestrator,wherein the work product represents a completion of processing of theworkload by the at least one accelerator device.

Example 26 includes the subject matter of any of Examples 17-25, andwherein transmitting a workload to the at least one accelerator devicecomprises transmitting the workload in-band, through a secure andauthenticated channel.

Example 27 includes the subject matter of any of Examples 17-26, andwherein determining at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises determining, based on an accelerator devicedirectory, the at least one accelerator device capable of processing aworkload in accordance with the at least one request parameter.

Example 28 includes the subject matter of any of Examples 17-27, andwherein the determined accelerator device is hosted on a remote devicedifferent from the compute device.

Example 29 includes the subject matter of any of Examples 17-28, andwherein determining the at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises sending the workload processing request to anorchestrator server; and receiving, from the orchestrator server, anidentification of the at least one accelerator device capable ofprocessing the workload.

Example 30 includes the subject matter of any of Examples 17-29, andwherein the at least one request parameter includes metadata indicativeof whether two or more portions of the workload can be acceleratedconcurrently.

Example 31 includes the subject matter of any of Examples 17-30, andwherein the at least one request parameter includes metadata indicativeof a target quality of service associated with the workload.

Example 32 includes the subject matter of any of Examples 17-31, andwherein the metadata is indicative of at least one of a target latencyor a target throughput associated with the workload.

Example 33 includes one or more machine-readable storage mediacomprising a plurality of instructions stored thereon that, in responseto being executed, cause a compute device to perform the method of anyof Examples 17-32.

Example 34 includes a compute device comprising means for performing themethod of any of Examples 17-32.

Example 35 includes a compute device comprising manager circuitry toreceive a workload processing request, the workload processing requestdefined by at least one request parameter; determine at least oneaccelerator device capable of processing a workload in accordance withthe at least one request parameter; transmit a workload to the at leastone accelerator device; receive a work product produced by the at leastone accelerator device from the workload; and provide the work productto an application.

Example 36 includes the subject matter of Example 35, and wherein thecompute device includes a pooled system management engine (PSME),wherein the PSME operates in an out-of-band capacity with respect to thecompute device, and the management circuitry is to operate in anout-of-band capacity with respect to the compute device, and wherein toreceive the workload processing request comprises to receive theworkload processing request without utilizing a host processor andwithout utilizing a host operating system of the compute device, todetermine the at least one accelerator device capable of processing theworkload comprises to determine the at least one accelerator devicewithout utilizing the host processor and without utilizing the hostoperating system of the compute device, to transmit the workload to theat least one accelerator device comprises to transmit the workloadwithout utilizing the host processor and without utilizing the hostoperating system of the compute device, to receive the work productproduced by the at least one accelerator device comprises to receive thework product without utilizing the host processor and without utilizingthe host operating system of the compute device, and to provide the workproduct to the application comprises to provide the work product to theapplication without utilizing the host processor and without utilizingthe host operating system of the compute device.

Example 37 includes the subject matter of any of Examples 35 and 36, andwherein to determine the at least one accelerator device capable ofprocessing the workload comprises to determine at least one otheraccelerator device, the at least one other accelerator device beinghosted on the compute device that also hosts the PSME.

Example 38 includes the subject matter of any of Examples 35-37, andwherein to receive the workload processing request comprises to receivethe workload processing request from an application executed on thecompute device.

Example 39 includes the subject matter of any of Examples 35-38, andwherein the manager circuitry is further to parse the workloadprocessing request for the at least one request parameter, and whereinthe at least one request parameter corresponds to a service-levelagreement (SLA) requirement.

Example 40 includes the subject matter of any of Examples 35-39, andwherein the manager circuitry is further to generate an acceleratordevice directory, wherein the accelerator device directory stores anaccelerator device identifier identifying the at least one acceleratordevice in correlation with configuration data and an accelerator sledidentifier for the at least one accelerator device, and wherein theconfiguration data is indicative of a number of operations per secondthat the at least one accelerator device is capable of performing, afunction that the at least one accelerator device is capable ofaccelerating, and a present utilization of the at least one acceleratordevice.

Example 41 includes the subject matter of any of Examples 35-40, andwherein the manager circuitry is further to identify a configurationparameter of the at least one accelerator device from the acceleratordevice directory; and determine that the configuration parameterrepresents a capability of the at least one accelerator device toprocess the workload.

Example 42 includes the subject matter of any of Examples 35-41, andwherein the manager circuitry is further to retrieve an accelerator sledidentifier for the at least one accelerator device; further comprisingnetwork communication circuitry to transmit an accelerator devicerequest to an orchestrator, wherein the accelerator device requestincludes a request to transmit the workload to the at least oneaccelerator device associated with the accelerator sled identifier.

Example 43 includes the subject matter of any of Examples 35-42, andwherein the network communication circuitry is further to receive thework product from the orchestrator, the work product representing acompletion of processing of the workload by the at least one acceleratordevice.

Example 44 includes the subject matter of any of Examples 35-43, andwherein to transmit a workload to the at least one accelerator devicecomprises to transmit the workload in-band, through a secure andauthenticated channel.

Example 45 includes the subject matter of any of Examples 35-44, andwherein to determine at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises to determine, based on an accelerator devicedirectory, the at least one accelerator device capable of processing aworkload in accordance with the at least one request parameter.

Example 46 includes the subject matter of any of Examples 35-45, andwherein the determined accelerator device is hosted on a remote devicedifferent from the compute device.

Example 47 includes the subject matter of any of Examples 35-46, andwherein to determine the at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises to send the workload processing request to anorchestrator server; and receive, from the orchestrator server, anidentification of the at least one accelerator device capable ofprocessing the workload.

Example 48 includes the subject matter of any of Examples 35-47, andwherein the at least one request parameter includes metadata indicativeof whether two or more portions of the workload can be acceleratedconcurrently.

Example 49 includes the subject matter of any of Examples 35-48, andwherein the at least one request parameter includes metadata indicativeof a target quality of service associated with the workload.

Example 50 includes the subject matter of any of Examples 35-49, andwherein the metadata is indicative of at least one of a target latencyor a target throughput associated with the workload.

Example 51 includes a compute device comprising circuitry for receivinga workload processing request, the workload processing request definedby at least one request parameter; means for determining at least oneaccelerator device capable of processing a workload in accordance withthe at least one request parameter; circuitry for transmitting aworkload to the at least one accelerator device; circuitry for receivinga work product produced by the at least one accelerator device from theworkload; and circuitry for providing the work product to anapplication.

Example 52 includes the subject matter of Example 51, and wherein thecircuitry for receiving the workload processing request comprisescircuitry for receiving the workload processing request withoututilizing a host processor and without utilizing a host operating systemof the compute device, the means for determining the at least oneaccelerator device capable of processing the workload comprisescircuitry for determining the at least one accelerator device withoututilizing the host processor and without utilizing the host operatingsystem of the compute device, the circuitry for transmitting theworkload to the at least one accelerator device comprises circuitry fortransmitting the workload without utilizing the host processor andwithout utilizing the host operating system of the compute device, thecircuitry for receiving the work product produced by the at least oneaccelerator device comprises circuitry for receiving the work productwithout utilizing the host processor and without utilizing the hostoperating system of the compute device, and the circuitry for providingthe work product to the application comprises circuitry for providingthe work product to the application without utilizing the host processorand without utilizing the host operating system of the compute device.

Example 53 includes the subject matter of any of Examples 51 and 52, andwherein the means for determining the at least one accelerator devicecapable of processing the workload comprises circuitry for determiningat least one other accelerator device, the at least one otheraccelerator device being hosted on the compute device that also hoststhe PSME device.

Example 54 includes the subject matter of any of Examples 51-53, andwherein the circuitry for receiving the workload processing requestcomprises circuitry for receiving the workload processing request froman application executing on the compute device.

Example 55 includes the subject matter of any of Examples 51-54, andfurther including circuitry for parsing the workload processing requestfor the at least one request parameter, wherein the at least one requestparameter corresponds to a service-level agreement (SLA) requirement.

Example 56 includes the subject matter of any of Examples 51-55, andfurther including circuitry for generating an accelerator devicedirectory, wherein the accelerator device directory stores anaccelerator device identifier identifying the at least one acceleratordevice in correlation with configuration data and an accelerator sledidentifier for the at least one accelerator device, and wherein theconfiguration data is indicative of a number of operations per secondthat the at least one accelerator device is capable of performing, afunction that the at least one accelerator device is capable ofaccelerating, and a present utilization of the at least one acceleratordevice.

Example 57 includes the subject matter of any of Examples 51-56, andfurther including circuitry for identifying a configuration parameter ofthe at least one accelerator device from the accelerator devicedirectory; and determining that the configuration parameter represents acapability of the at least one accelerator device to process theworkload.

Example 58 includes the subject matter of any of Examples 51-57, andfurther including circuitry for retrieving an accelerator sledidentifier for the at least one accelerator device; and transmitting anaccelerator device request to an orchestrator, wherein the acceleratordevice request includes a request to transmit the workload to the atleast one accelerator device associated with the accelerator sledidentifier.

Example 59 includes the subject matter of any of Examples 51-58, andfurther including circuitry for receiving the work product from theorchestrator, wherein the work product represents a completion ofprocessing of the workload by the at least one accelerator device.

Example 60 includes the subject matter of any of Examples 51-59, andwherein the circuitry for transmitting a workload to the at least oneaccelerator device comprises circuitry for transmitting the workloadin-band, through a secure and authenticated channel.

Example 61 includes the subject matter of any of Examples 51-60, andwherein the means for determining at least one accelerator devicecapable of processing a workload in accordance with the at least onerequest parameter comprises means for determining, based on anaccelerator device directory, the at least one accelerator devicecapable of processing a workload in accordance with the at least onerequest parameter.

Example 62 includes the subject matter of any of Examples 51-61, andwherein the determined accelerator device is hosted on a remote devicedifferent from the compute device.

Example 63 includes the subject matter of any of Examples 51-62, andwherein the means for determining the at least one accelerator devicecapable of processing a workload in accordance with the at least onerequest parameter comprises circuitry for sending the workloadprocessing request to an orchestrator server; and circuitry forreceiving, from the orchestrator server, an identification of the atleast one accelerator device capable of processing the workload.

Example 64 includes the subject matter of any of Examples 51-63, andwherein the at least one request parameter includes metadata indicativeof whether two or more portions of the workload can be acceleratedconcurrently.

Example 65 includes the subject matter of any of Examples 51-64, andwherein the at least one request parameter includes metadata indicativeof a target quality of service associated with the workload.

Example 66 includes the subject matter of any of Examples 51-65, andwherein the metadata is indicative of at least one of a target latencyor a target throughput associated with the workload.

1. A system comprising: a compute device to: receive a workloadprocessing request, the workload processing request defined by at leastone request parameter; determine at least one accelerator device capableof processing a workload in accordance with the at least one requestparameter; transmit a workload to the at least one accelerator device;receive a work product produced by the at least one accelerator devicefrom the workload; and provide the work product [[to]] for access by avirtual machine or container.
 2. The system of claim 1, wherein: toreceive the workload processing request comprises to receive theworkload processing request without utilizing a host processor andwithout utilizing a host operating system of the compute device
 3. Thesystem of claim 1, wherein: to determine the at least one acceleratordevice capable of processing the workload comprises to determine the atleast one accelerator device without utilizing the host processor andwithout utilizing the host operating system of the compute device. 4.The system of claim 1, wherein: to transmit the workload to the at leastone accelerator device comprises to transmit the workload withoututilizing the host processor and without utilizing the host operatingsystem of the compute device.
 5. The system of claim 1, wherein: toreceive the work product produced by the at least one accelerator devicecomprises to receive the work product without utilizing the hostprocessor and without utilizing the host operating system of the computedevice.
 6. The system of claim 1, wherein: to provide the work productto the application comprises to provide the work product to theapplication without utilizing the host processor and without utilizingthe host operating system of the compute device.
 7. The system of claim1, wherein the at least one request parameter corresponds to aservice-level agreement (SLA) requirement.
 8. The system of claim 1,wherein: the compute device is further to generate an accelerator devicedirectory, wherein the accelerator device directory stores anaccelerator device identifier identifying the at least one acceleratordevice in correlation with configuration data and the accelerator sledidentifier for the at least one accelerator device, and wherein theconfiguration data is indicative of a number of operations per secondthat the at least one accelerator device is capable of performing, afunction that the at least one accelerator device is capable ofaccelerating, and a present utilization of the at least one acceleratordevice.
 9. The system of claim 8, wherein the compute device is furtherto: identify a configuration parameter of the at least one acceleratordevice from the accelerator device directory; and determine that theconfiguration parameter represents a capability of the at least oneaccelerator device to process the workload.
 10. The system of claim 1,wherein to determine at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter comprises to determine, based on an accelerator devicedirectory, the at least one accelerator device capable of processing aworkload in accordance with the at least one request parameter.
 11. Oneor more non-transitory machine-readable storage media comprising aplurality of instructions stored thereon that, in response to beingexecuted, cause a compute device to: receive a workload processingrequest, the workload processing request defined by at least one requestparameter; determine at least one accelerator device capable ofprocessing a workload in accordance with the at least one requestparameter; transmit a workload to the at least one accelerator device;receive a work product produced by the at least one accelerator devicefrom the workload; and provide the work product for access by a virtualmachine or container.
 12. The one or more non-transitorymachine-readable storage media of claim 11, wherein the at least onerequest parameter corresponds to a service-level agreement (SLA)requirement.